An Internet of Things-based urban greening management system and method

Abstract

The present invention discloses an Internet of Things-based urban greening management system and method, which relates to the technical field of information processing, evaluates the growth changes of greening plants during the unit supervision period, collects the attribute tags of several plants and the evaluation values of plant growth changes, obtains the preferred plant attribute tags under different workloads of different greening maintenance tasks, establishes the preferred strategies for greening plants under different workload conditions, obtains the preferred attribute tags according to the expected workload of the greening maintenance tasks in the green belt area, provides reasonable reference suggestions, is conducive to selecting plants with similar habits in the same green belt, and reduces the waste of resources caused by the additional maintenance work for special greening plants. Moreover, for the to-be-planned greening area, by obtaining the plants within the capacity of the greening maintenance work, the additional burden generated during the later maintenance process can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of information processing, and specifically to an urban greening management system and method based on the Internet of Things. Background Art

[0002] Good urban greening helps to improve the urban ecological environment, affecting the quality of life of citizens and the sustainable development of the city. In the urban environment, human activities have a direct impact on the growth of plants. For example, during the plant growth cycle, multiple maintenance tasks such as watering, fertilizing, pruning, weeding, and pest control are carried out.

[0003] In traditional greening design schemes, mainly the requirements of the plant's own conditions such as color matching and shape pruning are considered, and the impact on the local environment of the growth environment of human interference objects is insufficiently considered. In the same maintenance strategy, the growth of different plants in the same green belt varies significantly, and some plant species are more prone to diseases, which easily pose safety hazards to the surrounding plants and the living environment of citizens. Summary of the Invention

[0004] The purpose of the present invention is to provide one to solve the problems raised in the prior art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: An urban greening management method based on the Internet of Things:

[0006] Step S100: Obtain the historical records of greening maintenance work in the green belt area, and calculate the evaluation value of the workload per unit green belt area;

[0007] Step S200: Collect data on the leaf density and leaf color uniformity of greening plants in the green belt, and evaluate the growth status of the greening plants;

[0008] Step S300: Evaluate the growth changes of greening plants during a unit supervision period, and establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants;

[0009] Step S400: Label the plant attributes of various types of plants in the green belt area through attribute tags, collect the attribute tags and plant growth change evaluation values of several plants, and obtain the preferred plant attribute tags under different workloads of greening maintenance work;

[0010] Step S500: Obtain a to-be-planned green belt area, obtain the preferred attribute tags according to the expected workload of the greening maintenance work in the green belt area, and recommend the plants including the attribute tags to relevant management personnel.

[0011] Further, step S100 includes:

[0012] Step S101: Set a certain greening maintenance area as a feature area, obtain the area range D of the feature area, and set all the construction records of greening maintenance during the unit supervision period of time length T1. The area range includes a planar range or a three-dimensional space range;

[0013] Step S102: Obtain the workload W of greening maintenance during a certain unit supervision period from the construction records, and calculate the workload evaluation value η of the feature area, where η = W / (D × T1).

[0014] Further, step S200 includes:

[0015] Step S201: Obtain the image information of the feature area to get the first feature image. The image information includes a visual image and a spectral image. Extract the image of the plant leaves from the first feature image to get the second feature image;

[0016] Step S202: Perform rasterization processing on the second feature image, divide the second feature image into n unit images, separately isolate the data values corresponding to each color channel in all the unit images, separately obtain the variances of the data values corresponding to each color channel, and calculate the color evaluation value v of the second feature image, , where m represents the total number of color channels in the image information, and uc i represents the variance of the data values corresponding to the i-th color channel in the second feature image;

[0017] Divide the second feature image into n unit images, extract the data of the same color channel in the n unit images, calculate the degree of dispersion of the color channels in the n images, and accumulate the degrees of dispersion of the m color channels to obtain the degree of dispersion description value of the second feature image;

[0018] Step S203: Calculate the degree of dispersion evaluation value α through the degree of dispersion evaluation function F(v), where α = F(v), and , e is the natural constant, and a and k are adjustment coefficients, satisfying the conditions a > 0 and k > 0;

[0019] The larger the variance, the greater the degree of dispersion of the data. In the solution, it is necessary to select the case with a smaller variance through a decreasing function. That is, in the plant evaluation coefficient, the larger the variance, the smaller the degree of dispersion evaluation value;

[0020] The variance of the input function is always greater than or equal to 0, so α is always greater than or equal to 0;

[0021] a controls the change amplitude of the function, and k controls the initial value. By controlling the magnitudes of a and k, the weight of α in the calculation process of the plant evaluation coefficient is controlled;

[0022] Step S204: Obtain the area pro1 of the first feature image and the area pro2 of the second feature image, and calculate the area evaluation value β of the first image, where β = pro2 / pro1;

[0023] Step S205: Calculate the plant evaluation coefficient PL in the first feature image, where PL = α × β.

[0024] Further, step S300 includes:

[0025] Step S301: Obtain the first first feature image at the start time of a certain unit supervision period, obtain the plant evaluation coefficient PL1 of the first first feature image, obtain the second first feature image at the end time of a certain unit supervision period, and obtain the plant evaluation coefficient PL2 of the second first feature image;

[0026] Step S302: Calculate the plant growth evaluation value GR of a certain unit supervision period, where GR = PL2 - PL1;

[0027] Step S303: Obtain the total number R of plant species in the area range D, respectively obtain the growth evaluation value of each plant species, and obtain the maximum value GR in the growth evaluation value max , take GR max as the reference evaluation value of the workload evaluation value η, and record the plant species corresponding to the reference evaluation value as the reference plant species;

[0028] Step S304: Set the plant evaluation benchmark value PG, where PG > 0, incorporate all plant species in the area range D excluding the reference plant species into the plant evaluation set, and denote the evaluation index of the f-th plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the f-th plant species;

[0029] Step S305: Take the plant evaluation benchmark value as the evaluation index of the reference plant species, and gather the evaluation indexes of all plant species to obtain the evaluation list of the workload evaluation value η.

[0030] Further, step S400 includes:

[0031] Step S401: Obtain the attribute labels of all plant species in the area range D, where the attribute label set pro b , pro b includes att1, att2, att3, …… and att x , where att1, att2, att3, …… and att x respectively represent pro bthe first, second, third, ……, and xth tags in

[0032] Step S402: Take a certain attribute tag as the target tag, obtain the set of all attribute tags including the target tag, further obtain the attribute tags and their corresponding plant species respectively, calculate the average value of the evaluation indices of the plant species, and obtain the tag evaluation value of the target tag under the workload evaluation value η.

[0033] Different plant species may include the same plant attribute tags. Pool the evaluation values of the same tag in different plant species to obtain the evaluation value of this tag. This evaluation value has a corresponding relationship with the workload evaluation value. By comparing the evaluation values of different tags with the same workload evaluation value, the tags are optimized.

[0034] Step S403: Pool the tag evaluation values of the target tag under several different workload evaluation values to obtain the tag evaluation list of the target tag.

[0035] Further, step S500 includes:

[0036] Step S501: Set a certain green belt planning area as the target area. The target area and the characteristic area belong to the same city. Obtain the area range D1 of the target area and the planned workload W1 of the greening maintenance of the target area within the unit supervision cycle time, and calculate the workload prediction value ω of the target area, ω = W1 / (D1 × T1).

[0037] Step S502: Obtain the alternative plants planted in the target area, obtain the tag evaluation values of all attribute tags of any one of the alternative plants under the workload prediction value ω, and sum the tag evaluation values to obtain the plant evaluation value of the alternative plant.

[0038] Step S503: Pool the corresponding plant evaluation values of all alternative plants, arrange the alternative plants in descending order according to the plant evaluation values to obtain the plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

[0039] To better implement the above method, an urban greening management system based on the Internet of Things is also proposed. The system includes:

[0040] A workload management module, a plant evaluation module, a growth relationship evaluation module, a plant attribute optimization module, and a plant recommendation module. Among them, the workload management module is used to record the history of greening maintenance work in the green belt area and calculate the evaluation value of the workload per unit green belt area. The plant evaluation module is used to evaluate the growth characteristics of plants in the greening area. The growth relationship evaluation module is used to establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants. The plant attribute optimization module is used to manage the preferred plant attribute tags under the workload of different greening maintenance work. The plant recommendation module is used to obtain the preferred attribute tags and recommend the plants including the attribute tags to relevant management personnel;

[0041] Further, the workload management module includes: a historical data management unit and a workload evaluation unit. Among them, the historical data management unit is used to obtain the construction records and unit supervision periods of greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area;

[0042] Further, the plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit, and an evaluation coefficient calculation unit. Among them, the feature image acquisition unit is used to obtain the first feature image and the second feature image of the greening area. The color evaluation value calculation unit is used to obtain the data values of each color channel in the image and calculate the color evaluation value of the second feature image. The evaluation value calculation unit is used to calculate the dispersion degree evaluation value and the area evaluation value. The evaluation coefficient calculation unit is used to calculate the plant evaluation coefficient in the first feature image;

[0043] Further, the growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit, and an evaluation list management unit. The growth evaluation value management unit is used to obtain the plant evaluation coefficient and calculate the plant growth evaluation value. The evaluation index calculation unit is used to calculate the evaluation index of each plant species. The evaluation list management unit is used to manage the evaluation list corresponding to the workload;

[0044] Further, the plant attribute optimization module includes: a tag management unit, a tag evaluation value management unit, and a tag evaluation list management unit. Among them, the tag management unit is used to manage the attribute tags of plants. The tag evaluation value management unit is used to calculate the tag evaluation value of the attribute tags under a certain workload evaluation value condition. The tag evaluation list management unit is used to manage the tag evaluation list of the tags;

[0045] Further, the plant recommendation module includes: a workload estimation unit, a plant evaluation unit, and a recommendation management unit. Among them, the workload estimation unit is used to obtain the workload estimation value. The plant evaluation unit is used to calculate the plant evaluation value of the alternative plants. The recommendation management unit is used to obtain the plant recommendation sequence and recommend the plant recommendation sequence to the green belt management personnel.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: By analyzing the impact of greening maintenance work on plant growth, a mathematical model of plant growth under different greening maintenance workloads is established. Further analyze the correlation between plant attributes and greening maintenance workload, establish an optimal selection strategy for greening plants under different workload conditions, and provide reasonable reference suggestions. It is beneficial to select plants with similar habits in the same green belt, reducing the waste of resources caused by additional maintenance work for special greening plants. And for the greening area to be planned, by obtaining plants within the capacity range of greening maintenance work, the additional burden generated during the later maintenance process can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic structural diagram of a city greening management system based on the Internet of Things according to the present invention;

[0048] Figure 2 It is a schematic flow diagram of a city greening management method based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] Embodiment: As Figure 1 and Figure 2 shown, the present invention provides a technical solution, a city greening management system and method based on the Internet of Things, and the method includes:

[0051] Step S100: Obtain the historical records of greening maintenance work in the green belt area, and calculate the evaluation value of the workload per unit green belt area;

[0052] Among them, step S100 includes:

[0053] Step S101: Set a certain greening maintenance area as a characteristic area, obtain the area range D of the characteristic area, and set all the construction records of greening maintenance during the unit supervision period of the time length T1. The area range includes the plane range or the three-dimensional space range;

[0054] Step S102: Obtain the workload W of greening maintenance during a certain unit supervision period from the construction records, and calculate the workload evaluation value η of the characteristic area, η = W / (D × T1);

[0055] In the embodiment, the workload of the greening maintenance work is collected and uploaded through the Internet of Things coefficient, and the workload of the greening maintenance work is aggregated and managed by establishing an Internet of Things information management platform.

[0056] Step S200: Collect data on the leaf density and the uniformity of leaf color of the greening plants in the green belt, and evaluate the growth status of the greening plants;

[0057] Among them, step S200 includes:

[0058] Step S201: Obtain the image information of the feature area to get the first feature image. The image information includes a visual image and a spectral image. Extract the image of the plant leaves from the first feature image to get the second feature image;

[0059] Step S202: Perform rasterization processing on the second feature image, divide the second feature image into n unit images, separately separate the data values corresponding to each color channel in all unit images, respectively obtain the variances of the data values corresponding to each color channel, and calculate the color evaluation value v of the second feature image, , where m represents the total number of color channels in the image information, and uc i represents the variance of the data value corresponding to the i-th color channel in the second feature image;

[0060] In the embodiment, the RGB channel or HSV channel of the visual picture can be used, or the multi-spectral channel of the multi-spectral picture can be used. When using the RGB channel or HSV channel, m takes 3. When using the multi-spectral picture, the value is taken according to the actual number of spectral channels, usually taking a value in the range between 3 and 10;

[0061] Step S203: Calculate the dispersion evaluation value α through the dispersion evaluation function F(v), α = F(v), where, , e is the natural constant, and a and k are adjustment coefficients, satisfying the conditions a > 0 and k > 0;

[0062] Step S204: Obtain the area pro1 of the first feature image and the area pro2 of the second feature image, and calculate the area evaluation value β of the first image, β = pro2 / pro1;

[0063] Step S205: Calculate the plant evaluation coefficient PL in the first feature image, PL = α × β.

[0064] Step S300: Evaluate the growth changes of the greening plants in the unit supervision period, and establish an evaluation list of the workload of the greening maintenance work and the growth changes of the greening plants;

[0065] Among them, step S300 includes:

[0066] Step S301: Obtain the first first characteristic image at the start moment of a certain unit supervision period, obtain the plant evaluation coefficient PL1 of the first first characteristic image, obtain the second first characteristic image at the end moment of a certain unit supervision period, and obtain the plant evaluation coefficient PL2 of the second first characteristic image;

[0067] Step S302: Calculate the plant growth evaluation value GR of a certain unit supervision period, GR = PL2 - PL1;

[0068] Step S303: Obtain the total number R of plant species in the area range D, respectively obtain the growth evaluation values of each plant species, and obtain the maximum value GR in the growth evaluation values max , take GR max as the reference evaluation value of the workload evaluation value η, and record the plant species corresponding to the reference evaluation value as the reference plant species;

[0069] Step S304: Set the plant evaluation benchmark value PG, where PG > 0, incorporate all plant species in the area range D excluding the reference plant species into the plant evaluation set, and record the evaluation index of the f-th plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the f-th plant species;

[0070] Step S305: Take the plant evaluation benchmark value as the evaluation index of the reference plant species, gather the evaluation indexes of all plant species, and obtain the evaluation list of the workload evaluation value η.

[0071] Step S400: Label the plant attributes of various plant species in the green belt area through attribute tags, gather the attribute tags and plant growth change evaluation values of several plant species, and obtain the preferred plant attribute tags under different workloads of greening maintenance work;

[0072] Among them, Step S400 includes:

[0073] Step S401: Obtain the attribute tags of all plant species in the area range D, where the attribute tag set pro b , pro b includes att1, att2, att3,..., and att x , where att1, att2, att3,..., and att x respectively represent the 1st, 2nd, 3rd,..., and x-th tags in pro b ;

[0074] Step S402: Take a certain attribute label as the target label, obtain the set of all attribute labels including the target label, further obtain the attribute labels and their corresponding plant species respectively, calculate the average value of the evaluation indices of the plant species, and obtain the label evaluation value of the target label under the workload evaluation value η;

[0075] Step S403: Aggregate the label evaluation values of the target label under several different workload evaluation values to obtain the label evaluation list of the target label;

[0076] In the embodiment, there are 4 types of plants, plant1, plant2, plant3, and plant4, and the evaluation indices are: dex1, dex2, dex3, and dex4 respectively. The sets of attribute labels are: pro1 (att2, att3, att4), pro2 (att1, att3, att5), pro3 (att1, att3, att5), and pro4 (att1, att3, att5);

[0077] The label evaluation values are: label1 = (dex2 + dex3) / 2, label2 = dex1,

[0078] label3 = (dex1 + dex2 + dex3 + dex4) / 4, label4 = dex1,

[0079] label4 = (dex2 + dex3 + dex4) / 3.

[0080] Step S500: Obtain a certain green belt area to be planned, obtain the preferred attribute labels according to the expected workload of the greening maintenance work in the green belt area, and recommend the plants including the attribute labels to the relevant management personnel;

[0081] Among them, Step S500 includes:

[0082] Step S501: Set a certain green belt planning area as the target area. The target area and the characteristic area belong to the same city. Obtain the area range D1 of the target area and the planned workload W1 of the greening maintenance of the target area within the unit supervision cycle time, and calculate the workload prediction value ω of the target area, ω = W1 / (D1 × T1);

[0083] Step S502: Obtain the alternative plants to be planted in the target area, obtain the label evaluation values of all the attribute labels of any one alternative plant under the workload prediction value ω, and sum the label evaluation values to obtain the plant evaluation value of the alternative plant;

[0084] Step S503: Collect the corresponding plant evaluation values of all alternative plants, arrange the alternative plants according to the order of the plant evaluation values from high to low to obtain a plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

[0085] The system includes: a workload management module, a plant evaluation module, a growth relationship evaluation module, a plant attribute optimization module, and a plant recommendation module;

[0086] Among them, the workload management module is used for the historical records of greening maintenance work in the green belt area, and calculates the evaluation value of the workload in the unit green belt area. Among them, the workload management module includes: a historical data management unit and a workload evaluation unit. Among them, the historical data management unit is used to obtain the construction records and unit supervision cycle of greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area;

[0087] Among them, the plant evaluation module is used to evaluate the plant growth characteristics in the greening area. Among them, the plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit, and an evaluation coefficient calculation unit. Among them, the feature image acquisition unit is used to obtain the first feature image and the second feature image of the greening area, the color evaluation value calculation unit is used to obtain the data values of each color channel in the image and calculate the color evaluation value of the second feature image, the evaluation value calculation unit is used to calculate the dispersion degree evaluation value and the area evaluation value, and the evaluation coefficient calculation unit is used to calculate the plant evaluation coefficient in the first feature image;

[0088] Among them, the growth relationship evaluation module is used to establish an evaluation list of the workload of greening maintenance work and the growth changes of greening plants. Among them, the growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit, and an evaluation list management unit. The growth evaluation value management unit is used to obtain the plant evaluation coefficient to calculate the plant growth evaluation value, the evaluation index calculation unit is used to calculate the evaluation index of each plant species, and the evaluation list management unit is used to manage the evaluation list corresponding to the workload;

[0089] Among them, the plant attribute optimization module is used to manage the preferred plant attribute labels under the workload of different greening maintenance work. Among them, the plant attribute optimization module includes: a label management unit, a label evaluation value management unit, and a label evaluation list management unit. Among them, the label management unit is used to manage the attribute labels of plants, the label evaluation value management unit is used to calculate the label evaluation value of the attribute label under a certain workload evaluation value condition, and the label evaluation list management unit is used to manage the label evaluation list of the label;

[0090] Among them, the plant recommendation module is used to obtain preferred attribute tags and recommend plants including the attribute tags to relevant management personnel. Among them, the plant recommendation module includes: a workload estimation unit, a plant evaluation unit, and a recommendation management unit. Among them, the workload estimation unit is used to obtain a workload estimate value, the plant evaluation unit is used to calculate the plant evaluation value of alternative plants, and the recommendation management unit is used to obtain a plant recommendation sequence and recommend the plant recommendation sequence to the green belt management personnel.

[0091] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. An Internet of Things-based urban greening management method, characterized in that: The method includes the steps of: Step S100: Obtain the historical records of the greening maintenance work in the green belt area, and calculate the evaluation value of the workload per unit green belt area; Step S200: Collect the data of the leaf density and the leaf color uniformity of the greening plants in the green belt, and evaluate the growth status of the greening plants; Step S300: Evaluate the growth changes of the greening plants in a unit supervision period, and establish an evaluation list of the workload of the greening maintenance work and the growth changes of the greening plants; Step S300 includes: Step S301: Obtain the first first feature image at the start time of a certain unit supervision period, obtain the plant evaluation coefficient PL1 of the first first feature image, obtain the second first feature image at the end time of the certain unit supervision period, and obtain the plant evaluation coefficient PL2 of the second first feature image; Step S302: Calculate the plant growth evaluation value GR of the certain unit supervision period, GR = PL2 - PL1; Step S303: Obtain the total number R of plant species in the area range D, respectively obtain the growth evaluation values of each plant species, and obtain the maximum value GR among the growth evaluation values max , and use GR max as the reference evaluation value of the workload evaluation value η, and record the plant species corresponding to the reference evaluation value as the reference plant species; Step S304: Set the plant evaluation benchmark value PG, where PG > 0. Incorporate all plant species in the regional range D excluding the reference plant species into the plant evaluation set, and denote the evaluation index of the f-th plant species in the plant evaluation set as bot f , , where GR f is the evaluation index of the f-th plant species; Step S305: Use the plant evaluation benchmark value as the evaluation index of the reference plant species, gather the evaluation indexes of all plant species, and obtain the evaluation list of the workload evaluation value η; Step S400: Label the plant attributes of various types of plants in the green belt area through attribute tags, gather the attribute tags and the plant growth change evaluation values of several types of plants, and obtain the preferred plant attribute tags under different workloads of the greening maintenance work; Step S400 includes: Step S401: Obtain the attribute tags of all types of plants in the area range D, where the set of attribute tags pro corresponding to the b-th plant b , pro b includes att1, att2, att3, …… and att x , where att1, att2, att3, …… and att x respectively represent the 1st, 2nd, 3rd, …… and x-th tags in pro b ; Step S402: Use a certain attribute tag as the target tag, obtain the set of all attribute tags including the target tag, further obtain the attribute tag and its corresponding plant species respectively, calculate the average value of the evaluation indexes of the plant species, and obtain the tag evaluation value of the target tag under the condition of the workload evaluation value η; Step S403: Gather the tag evaluation values of the target tag under several different workload evaluation value conditions, and obtain the tag evaluation list of the target tag; Step S500: Obtain a certain green belt area to be planned, obtain the preferred attribute tags according to the expected workload of the greening maintenance work in the green belt area, and recommend the plants including the attribute tags to the relevant management personnel.

2. The method for urban greening management based on the Internet of Things according to claim 1, characterized in that: Step S100 includes: Step S101: Set a certain greening maintenance area as the feature area, obtain the area range D of the feature area, and set all the construction records of the greening maintenance in the unit supervision period with the time length T1. The area range includes the plane range or the three-dimensional space range; Step S102: Obtain the workload W of the greening maintenance in a certain unit supervision period from the construction records, and calculate the workload evaluation value η of the feature area, η = W / (D × T1).

3. The method for urban greening management based on the Internet of Things according to claim 2, wherein: Step S200 includes: Step S201: Obtain the image information of the feature area to get the first feature image. The image information includes the visual image and the spectral image, and extract the image of the plant leaves from the first feature image to get the second feature image; Step S202: Perform rasterization processing on the second feature image, divide the second feature image into n unit images, separately extract the data values corresponding to each color channel in all unit images, respectively obtain the variances of the data values corresponding to each color channel, and calculate the color evaluation value v of the second feature image, , where m represents the total number of color channels in the image information, and uc i represents the variance of the data values corresponding to the i-th color channel in the second feature image; Step S203: Calculate the dispersion evaluation value α through the dispersion evaluation function F(v), α = F(v), where , e is the natural constant, and a and k are adjustment coefficients that satisfy the conditions a > 0 and k > 0; Step S204: Obtain the area pro1 of the first feature image and the area pro2 of the second feature image, and calculate the area evaluation value β of the first image, β = pro2 / pro1; Step S205: Calculate the plant evaluation coefficient PL in the first feature image, where PL = α × β.

4. The urban greening management method based on the Internet of Things according to claim 3, characterized in that: Step S500 includes: Step S501: Set a certain green belt planning area as the target area. The target area and the feature area belong to the same city. Obtain the area range D1 of the target area and the planned workload W1 of the greening maintenance in the target area within the unit supervision cycle time. Calculate the estimated workload ω of the target area, where ω = W1 / (D1 × T1); Step S502: Obtain the alternative plants planted in the target area. Obtain the label evaluation values of all attribute labels of any one of the alternative plants under the condition of the estimated workload ω, and sum the label evaluation values to obtain the plant evaluation value of the alternative plant; Step S503: Collect the corresponding plant evaluation values of all alternative plants. Arrange the alternative plants in descending order according to the plant evaluation values to obtain a plant recommendation sequence, and recommend the plant recommendation sequence to the green belt management personnel.

5. An Internet of Things-based urban greening management system for implementing an Internet of Things-based urban greening management method according to any one of claims 1-4, characterized in that: A workload management module, a plant evaluation module, a growth relationship evaluation module, a plant attribute optimization module, and a plant recommendation module. Among them, the workload management module is used for the historical records of the greening maintenance work in the green belt area and calculates the evaluation value of the workload in the unit green belt area. The plant evaluation module is used to evaluate the plant growth characteristics in the greening area. The growth relationship evaluation module is used to establish an evaluation list of the workload of the greening maintenance work and the growth changes of the greening plants. The plant attribute optimization module is used to manage the preferred plant attribute labels under the workload of different greening maintenance works. The plant recommendation module is used to obtain the preferred attribute labels and recommend the plants including the attribute labels to the relevant management personnel.

6. An Internet of Things-based urban greening management system according to claim 5, characterized in that: The workload management module includes: a historical data management unit and a workload evaluation unit. Among them, the historical data management unit is used to obtain the construction records and unit supervision cycle of the greening maintenance in the greening maintenance area, and the workload evaluation unit is used to calculate the workload evaluation value in the greening area; The plant evaluation module includes: a feature image acquisition unit, a color evaluation value calculation unit, an evaluation value calculation unit, and an evaluation coefficient calculation unit. Among them, the feature image acquisition unit is used to obtain the first feature image and the second feature image of the greening area. The color evaluation value calculation unit is used to obtain the data values of each color channel in the image and calculate the color evaluation value of the second feature image. The evaluation value calculation unit is used to calculate the dispersion evaluation value and the area evaluation value. The evaluation coefficient calculation unit is used to calculate the plant evaluation coefficient in the first feature image.

7. An Internet of Things-based urban greening management system according to claim 5, characterized in that: The growth relationship evaluation module includes: a growth evaluation value management unit, an evaluation index calculation unit, and an evaluation list management unit. The growth evaluation value management unit is used to obtain the plant evaluation coefficient and calculate the plant growth evaluation value. The evaluation index calculation unit is used to calculate the evaluation index for each plant species. The evaluation list management unit is used to manage the evaluation list corresponding to the workload. The plant attribute optimization module includes: a label management unit, a label evaluation value management unit, and a label evaluation list management unit. Among them, the label management unit is used to manage the attribute labels of plants. The label evaluation value management unit is used to calculate the label evaluation value of the attribute label under a certain workload evaluation value condition. The label evaluation list management unit is used to manage the label evaluation list of the label.

8. The urban greening management system based on the Internet of Things according to claim 5, characterized in that: The plant recommendation module includes: a workload estimation unit, a plant evaluation unit, and a recommendation management unit. Among them, the workload estimation unit is used to obtain the workload estimation value. The plant evaluation unit is used to calculate the plant evaluation value of the alternative plants. The recommendation management unit is used to obtain the plant recommendation sequence and recommend the plant recommendation sequence to the green belt management personnel.

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